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Nutrition and food science go genomic
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Vol.24 No.4 April 2006
Nutrition and food science go genomic Manuela J. Rist, Uwe Wenzel and Hannelore Daniel Molecular Nutrition Unit, Department Food and Nutrition, Technical University of Munich, Am Forum 5, D-85350 Freising-Weihenstephan, Germany
The wealth of genomic information and high-throughput profiling technologies are now being exploited by scientists in the disciplines of nutrition and food science. Diet and food components are prime environmental factors that affect the genome, transcriptome, proteome and metabolome, and this life-long interaction defines the health or disease state of an individual. For the first time the interaction of foods, and individual food constituents, with the biological systems can be defined on a molecular basis. Profiling technologies are used in basic-science applications for identifying the mode of action of foods or particular ingredients, and are similarly taken into the science-driven development of foods with a defined biofunctionality. Biomarker profiles and patterns derived from genomics applications in humans should guide nutrition and food science in developing evidence-based dietary recommendations and health-promoting foods.
Introduction In view of the emerging importance of nutrition in the development of chronic diseases, both academia and the food industry face a new challenge: the need to develop strategies and products that are not only safe but also contribute to health maintenance or can even prevent the development of specific diseases. Conceptually, this is achieved by identifying foods or food ingredients that affect the metabolic processes underlying disease initiation and/or progression and using this knowledge to develop products that functionally target metabolic perturbations. The wealth of genomic information, genomics-based technologies and model systems available provide a spectrum of new tools for use in the fields of human nutrition and food science. These new technologies are being used to study the molecular basis of the interaction of individual food constituents with both the genome and the metabolism of the human consumer. For historical reasons, nutrition and food science are not well prepared for exploiting genomics technologies, primarily because of the lack of appropriate teaching of human genetics, genomics and molecular biology in most university programs. However, these deficits have been recognized and, in response, numerous initiatives have been launched, recently, in Europe, Asia and the USA under the heading of ‘nutrigenomics’. Although, to some, nutrigenomics might represent just another ‘-omic’, it will change the face of the research in nutrition and food Corresponding author: Daniel, H. ([email protected]). Available online 20 February 2006
science by moving the genome into the centre of all the processes that essentially determine mammalian metabolism in health and disease [1,2]. Every nutritional process relies on the interplay of a large number of proteins, which are encoded by the respective mRNA molecules expressed in a certain cell, organ or organism. Alterations in mRNA levels and, in turn, of the corresponding protein levels (although these do not necessarily change in parallel) are crucial parameters in controlling the flux of a nutrient or metabolite through a biochemical pathway. Nutrients and non-nutrient components of foods, diets and lifestyle – including physical activity – can affect every step in the flow of genetic information from gene expression to protein synthesis and degradation and, thereby, alter metabolic functions in complex ways. There can be no doubt that the interplay of the somewhat static mammalian genome with its rapidly changing nutritional environment, represented by a huge spectrum of foods, is one of the most attractive and interesting areas in postgenomic research. We will use genomics as a collective term that covers the three sub-disciplines of transcriptomics, proteomics and metabolomics, and the term systems biology as the integrated approach for studying biological systems, at the level of cells, organs or organisms, by measuring and integrating genomic, proteomic and metabolic data. In this respect, it is the attempt to bring the existing knowledge about the various biological components onto a systemic level (with its unifying organizational principles) that determines the function of the organism. In this review, we will focus on the tools available to molecular food researchers and provide examples of what can be learned when nutrition ‘goes genomic’. The responses to food intake represent systems biology ‘par excellence’ It has been essential for the survival of mammals that they can adapt, quickly, to changes in their nutritional environment while retaining a sufficiently active metabolism to satisfy the requirement for a high rate of ATP production and to produce all the building blocks necessary for cell and tissue renewal and maintenance. Adaptation to food availability in terms of energy, in addition to individual nutrients, requires fast but also sustained responses that simultaneously change the numerous interconnected metabolic processes – cells need to regulate nutrient transport processes and storage capacity, tune the flux of intermediates through metabolic routes and branching points, and restructure the cellular transcriptome and proteome. The key question is whether
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Foods and diet Carbohydrates, Lipids, Proteins Fatty acids Retinoids Vitamin D3
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Figure 1. The complexity of the interplay of diet and foods with the genome that orchestrates human metabolism. The particular composition of a diet, in addition to individual foods or food constituents, can affect every step from epigenetic modifications of genes to changes in transcription, translation, protein degradation and the metabolome, by direct and indirect routes (e.g. diet-dependent hormone secretion, allosteric regulation). Profiling technologies, such as transcriptomics, proteomics and metabolomics, should enable the elucidation of the interactions of dietary constituents with the genome at every level of complexity and provide insights into the connectivity among the different layers of the biological processes.
the ‘omics’ technologies, combined with advanced data analysis and interpretation tools, enable understanding and reproduction of these physiological sensing and signal integration mechanisms and their multidimensional wiring. Assessing the metabolic responses to complex foods is, therefore, similar to looking at hundreds of test compounds, simultaneously, and observing diverse temporal and spatial responses. In this respect, nutrigenomics is clearly different from pharmacogenomic or toxicogenomic approaches where, in most cases, a single drug or xenobiotic is studied. The simplified scheme shown in Figure 1 represents the complexity of interactions among diet (or foods) and the mammalian genome, for example, epigenetic alterations of the DNA, effects on mRNA expression, control of the proteome, allosteric regulation and maintenance of metabolite pools – and their internal wiring – with feed-back and feed-forward loops. In the past, nutrition and food research focused almost exclusively on metabolites, which involved measuring the concentration of a few individual intermediates or markers, whereas the new molecular tools enable insight into virtually every step in the processing of the biological information: from DNA to mRNA to proteins and to metabolic function. However, there are important levels of biological regulation beyond gene and protein expression, for example, protein–protein interactions and alterations of protein activity by metabolic intermediates such as kinetic effects and allosteric modulation of function www.sciencedirect.com
(Figure 1). Consequently, whatever information is gathered at the mRNA and protein-expression levels will not enable sufficient predictions to be made on all metabolic consequences. The final stage along the line from gene to mRNA to protein to function is, therefore, the analysis of the pattern, and the concentrations, of the metabolites that flow between the proteins, organelles, cells and organs. That means that, ultimately, nutrition and food scientists will end up where they started – analyzing metabolites – but this time the analysis of the metabolome will comprise the sum of all detectable low- and intermediate-molecular-weight compounds rather than individual metabolites. The potential of comprehensive metabolic analysis, together with statistical methods of cluster analysis for discriminatory phenotype analysis, becomes obvious when reviewing the achievements in research into bacterial systems, yeast and plants – both wild-type and transgenic organisms [3–5]. The systems biology approach uses all genomic information, all expression information (at mRNA and protein level) and all metabolites to describe metabolism in the most comprehensive way, based on the networks of biological regulation.
Genomics applications in preclinical nutrition and food research The applications of transcriptomics, proteomics and metabolomics technologies in nutritional studies seem
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unlimited when it comes to basic and preclinical research in either cell cultures or animal models. The techniques have the potential to identify specific markers (biomarkers) that respond easily to a given nutrient, nonnutrient compound, treatment or diet, in a well-defined experimental setting. This might not mean that changes in mRNA or protein level can be taken as causal markers but rather as a pattern of expressed mRNAs, or a pattern of proteins, that changes in a characteristic but reproducible way. So far, biomarkers have been identified mainly by rational approaches based on the knowledge of metabolism; by contrast, the new screening processes are not knowledge guided, analyzing up to several thousand potentially affected indicators of the metabolic status of an individual, simultaneously. There are a growing number of examples of DNA microarray and proteome profiling technologies being used to identify the cellular responses to dietary constituents and for identifying their molecular targets. For example, studies on flavonoids, such as the green tea catechins [6,7] or soy isoflavones and flavones [8–11], try to link their bioactivity to the findings of epidemiological studies that suggest that secondary plant metabolites possess a variety of healthpromoting activities. Selected nutrients, including polyunsaturated fatty acids [12,13] or micronutrients such as zinc [14,15] and vitamin E [16], have also been studied in cell culture systems or laboratory animals – by transcriptome, proteome and metabolite profiling – to define the molecular mechanisms by which they affect metabolism, and for deriving signatures that describe a particular metabolic state. There can be no doubt that, in the near future, we will be overwhelmed with similar studies that investigate the biology of individual food constituents. Bioinformatics and chemometrics are essential to cope with the huge amounts of data already being generated, and more powerful tools need to be developed to facilitate analysis and interpretation of the results of comprehensive ‘omics’ studies. Current limitations of the ‘omics’ technologies when applied to studies in humans Although the applications of the different ‘omics’ technologies appear unlimited when using either cells in culture or model organisms, clearly there are restrictions on their use for studies in humans. Expression profiling at the level of mRNA or protein is limited by the availability of vital cells or tissues for analysis. Although certain tissues and cells, such as hair-follicle cells, skin cells or even exfoliated intestinal cells, can be obtained in sufficient quantities by non-invasive techniques, proteome or metabolome analysis cannot be carried out on the singlecell level, whereas expression analysis using quantitative PCR or array techniques can be performed on single cells. Different types of blood cells are, therefore, an interesting source of biological material and can be used as reporter cells in human studies. These cells respond to dietary fluctuations and, more interestingly, have different lifetimes, different gene expression profiles and control systems, and can reach and occupy different body compartments. In particular, human peripheral blood mononuclear cells are a source for the identification of www.sciencedirect.com
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mRNA or protein biomarkers, which potentially change in response to environmental factors, by using DNA-arrays or proteome analysis. The biological samples that can be obtained most easily in the required amount are urine or saliva (by non-invasive sampling) and serum or plasma (by minimal invasive approaches). Given that these biological fluids are essentially devoid of cells (and mRNA and DNA), only proteins and metabolites can be analyzed with the required robustness and sensitivity. Profiling technologies of body fluids for use in human studies Plasma and serum represent the fluid compartment that carries metabolites and signaling molecules to and from cells in the various organs and, therefore, serve as a distribution system in interorgan metabolism. By contrast, urine excretion serves to eliminate metabolic endproducts of no further use and contributes to electrolyte and water homeostasis. Nutrients and metabolic intermediates are found in urine only in trace quantities because of the efficient reabsorption mechanisms in the tubular cells along the nephrons in the kidney; however, their appearance in higher concentrations might reflect a disturbed homeostasis or impaired renal function. Although well established in bacterial, yeast and plant systems, metabolomics studies for assessing the effects of diet or food constituents in humans are sparse and are currently based mainly on NMR spectroscopy applications [17,18]. Metabolite profiling The most important techniques for metabolomic applications in human biofluids are gas chromatography (GC) or liquid chromatography (LC), coupled to mass spectrometry (MS) or NMR spectroscopy. Each of these platforms has advantages and limitations, which have been reviewed in detail elsewhere [19–21]. To obtain the most valuable information from the metabolome, a combination of different techniques appears necessary because each technique covers different mass and concentration ranges; for example, GC–MS is mainly used to identify and quantify low-molecular-weight metabolites with masses of up to w1000 Da. When applied to human biofluids, GC–MS has been used in toxicology, pharmacology and neonatal screening, and only a few selected metabolites are usually analyzed [22–24]. In nutrition studies, GC–MS has been used to determine the fate of specific food compounds or metabolites [25,26]. LC–MS seems better suited for the analysis of labile and low- to intermediate-molecular-weight compounds. It is the most global of the techniques described here and can be used to determine a raw metabolite profile, in addition to identifying and quantifying specific compounds. Absolute quantification, however, requires reliable internal standards and better results for identification can be obtained using tandem MS (LC–MS/MS). Several studies have been published using LC–MS for the analysis of selected compounds [26,27] or metabolite profiles [28]. High-resolution 1H-NMR spectroscopy can, in principle, detect all proton-containing metabolites present in a sample above a certain threshold concentration. Thus,
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one-dimensional 1H-NMR techniques are mainly used for metabolic fingerprinting of raw samples without identifying the individual metabolites. After chromatographic separation, or using two-dimensional techniques, NMR can also be used to characterize specific compounds or to obtain valuable structural information. NMR techniques have been used, successfully, to analyze metabolite changes in human and animal body fluids and tissue extracts under various conditions [29–32]. The huge amount of data generated from the described metabolomics technologies requires processing and analysis using appropriate chemometric methods. Following the necessary pre-processing of the spectra, relevant information is usually extracted and visualized using multivariate analysis methods such as hierarchical cluster analysis, principal components analysis or discriminate analysis [20,28]. Various efforts are being undertaken to standardize metabolomics experiments and build databases that will facilitate the identification of metabolites [33] (for the different initiatives see http:// www.niddk.nih.gov/fund/other/metabolomics2005/local. htm and http://www.nugo.org/metabolomics/13187). Despite great progress in the field of metabolomics, profiling techniques applied to human body fluids in nutrition and food research face some unique challenges (Box 1), which also require a special collaborative effort to advance the science.
Proteome profiling Classical two-dimensional gel-electrophoresis separations for the plasma proteome, combined with peptide-mass fingerprinting using matrix-assisted laser desorption/ ionization–time-of-flight–MS (MALDI–TOF–MS) for protein identification, in addition to shotgun approaches with protease digestion followed by LC-based MS technologies for peptide mapping, are all applied to define the plasma proteome [34–36]. In a huge consortium, under the umbrella of the Human Proteome Project (http://www. hupo.org), different proteome analysis techniques and methods for standardization and sample processing are being developed to define the human plasma proteome. The first human plasma proteome database (http://www. plasmaproteomedatabase.org) has been launched recently. This can serve as a reference for profiling and protein identification when plasma proteomics studies are carried out in nutrition and food research, although, except for a few examples using cell culture models or laboratory animals [8,37], such studies are currently lacking. However, in view of the numerous efforts to define the plasma proteome for a variety of clinical applications, the wealth of information gathered here deserves its use in nutrition and food research, particularly because a variety of plasma proteins can be used to describe the nutritional status of an individual. Moreover, the plasma sub-proteomes, formed by proteins and peptides released by individual organs and cell populations, might be useful to define the physiological state of these organs and/or cells and are, therefore, of particular relevance for nutrition and food research as diseaserelated entities [36]. www.sciencedirect.com
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Box 1. The subtleties of metabolomics approaches in human studies Human metabolism is essentially the expression of a transient steady state in the dynamics of the biosynthesis and degradation of proteins (turn-over) that function either as enzymes, receptors, transporters, channels, hormones and other signaling molecules or that provide structural elements for cells, organs or the skeleton. Between the proteins, there is a variable flow of metabolic intermediates, which serve as building blocks or provide the fuel for ATP synthesis, and these can be determined by metabolomics approaches. However, the metabolome is variable in space and time. Every organ, different cells within an organ and different intracellular compartments display different metabolite compositions, which, again, are different from the metabolite profiles in blood or other body fluids: every sample taken represents just a snapshot of an ever-changing metabolite profile. The variability of metabolite profiles appears much greater in humans than in model systems or laboratory animals [28,30,47]. There are many intrinsic and extrinsic factors affecting the metabolome, such as genotype, gender, hormonal status and age, in addition to dietary variation, physical activity, socio-economic status, cultural influences, medication, smoking, stress, pathologies and, importantly, the gut microflora with its enormous metabolic capacity. When metabolite profiling is applied to human body fluids, it has to be taken into account that plasma and urine are just flow-through compartments in which thousands of nutrients and metabolites provided by the diet after digestion, absorption and clearance by the liver are mixed with those released by the various organs and cell types, including those derived from microbial metabolism. Metabolomics, as applied to human studies, therefore faces the problem to sort out immediate diet effects and the effects of the microbial flora on the metabolite profiles, in whatever sample, to resolve the ‘endogenous fingerprint’. It, therefore, requires particularly well-defined experimental studies in volunteers kept under controlled feeding and/or starvation regimens in a standardized setting – a concept unique to this application of metabolomics. Moreover, there is a necessity to standardize the application of metabolomics to nutrition studies in terms of sample collection, preparation of fluids, times, volumes and processing aids. For all of this to happen, an international collaborative project needs to be launched.
The concept of metabolic-marker profiles in nutrition and food research Gomeostatic mechanisms in biological systems are characterized by hierarchical orders and multiple redundancies to maintain a given steady state for as long as possible. Any disturbance in the system is compensated for in space and time, and even the malfunction of a gene, a protein or even a whole pathway might be overcome without evident phenotypic alterations. Therefore, it is obvious that a single marker – whether a transcript, protein or metabolite – is often unable to provide sufficient information on the metabolic state. However, subtle changes, which reflect the biological redundancy in a large spectrum of mRNA species, protein entities or metabolites, can be observed and might, conceptually, as metabolic-marker profiles, describe the metabolic status representative of a given state of health or disease stage. It is becoming increasingly apparent that such early markers for disease initiation or progression can be identified by biomarker profiling and using different profiling technologies [38– 40]. Figure 2 depicts the concept of metabolic-marker profiles to describe the metabolic status of an individual in the course of disease development. Although only a few scientific publications have taken this concept to proof [29,41], there are numerous studies currently exploring
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Figure 2. Time-dependent changes of metabolic-marker profiles during progression from a healthy to a diseased state. Markers can be derived from transcriptome, proteome or metabolite profiling, or combinations thereof, and can improve diagnosis. Whenever the markers leave the range of natural variation, they are predicted to indicate early preclinical alterations, which might not be detected otherwise. At this stage, early interventions, such as a change in diet and physical activity or the application of functional foods, might possibly prevent the onset of disease, whereas in the pathological state, usually a pharmacological therapy is required.
metabolic-marker profiles in research projects linked to human nutrition and food effects on metabolism. For example, the metabolic effects of dietary isoflavones or chamomile ingestion have been studied using the NMR analysis of human biofluids [17,18]. Taking genomic technologies into product development There can be no doubt that genomic technologies will change the face of food research [42,43]. Although already in use in all areas of food biotechnology and microbiology, these profiling tools are only used, currently, in cell culture and laboratory animal studies in the process of developing supplements or food products that perform specific functions (functional food), including new transgenic plant varieties [7,10,12]. However, in view of an emerging demand for evidence-based health claims related to functional food [44,45], genomics-based platforms and biomarker-profiling techniques also need to be applied to human studies. Product development should be guided by scientific data and sound biological evidence, which might be derived from models of increasing biological complexity until finally including human intervention studies (Figure 3). It is agreed by expert panels and food authorities in a variety of countries [46] that the development of functional foods, and their safety assessments, should be conducted on a case-by-case basis using studies with good experimental design. The totality of scientific evidence should eventually enable agreement within the scientific community that an association between a food or a food component and a proposed health benefit is valid [45]. From this perspective, product development in the functional food era needs to apply www.sciencedirect.com
genomics technologies because they provide a comprehensive analysis of the biological functionality of a given ingredient, a mixture or a complex food, not only with respect to the targeted biochemical processes but also with respect to the identification of unwanted side effects in the overall safety assessment. Where do we stand, and where do we go from here? Genome–food interactions are the paradigm for the interplay between the human genome and its environment. Nutrition and food science are stepping into the genomics era, and it is becoming evident that nutrients and other food components are key factors in altering gene transcription, protein levels and functions, and the metabolome, which eventually translates into a health or disease state on the basis of a given genome. A wealth of genetic information, and novel techniques with highthroughput capabilities, will provide new tools for nutrition and food research. Knowledge regarding the response of mammalian organisms to changes in diet or in response to individual nutrients and non-nutrient components of foods can be gathered by expression arrays, proteome analysis and metabolite profiling technologies. Emerging technologies, such as two-dimensional magnetic resonance imaging, currently under development might also be applied. All of these techniques should enable the determination of metabolic markers that can guide the assessment of the health status of humans and provide measures for food-derived effects on human metabolism. In terms of the development of healthpromoting and functional foods that specifically target a metabolic process to slow disease progression and reduce
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Figure 3. Applications of genomics technologies when applied to the development of functional foods that target specific body functions through either individual ingredients or mixtures. Conceptually, a discovery process that derives molecular markers for the bioactivity of defined foods or food ingredients, and identifies their putative mode of action using appropriate cell models, should be followed by an intervention study in an animal model to verify and/or define the markers in vivo. A human intervention trial with a complex food that employs the biomarker profiling concept should follow for proof-of-concept and to substantiate health claims on a scientific basis. This process might well be iterative and it might be necessary to return to cell culture or animal models to find or validate relevant markers and to be able to substantiate the health claims arising from human intervention trials.
risk, metabolic-marker profiles need to guide product development in an evidence-based manner. The time has now arrived to take the new technologies into well-defined human intervention trials and large-scale cohort studies, with groups of defined genotypes, for proof-of-concept. Although these studies will be tedious and costly, in view of the rapidly evolving health problems in all developed countries, such as obesity, diabetes type II, cardiovascular diseases and cancers, with well-defined causes – an unhealthy lifestyle, overnutrition and lack of exercise –these studies seem to warrant concertive and collaborative action by academia and industry. References 1 Muller, M. and Kersten, S. (2003) Nutrigenomics: goals and strategies. Nat. Rev. Genet. 4, 315–322 2 van der Werf, M.J. et al. (2001) Nutrigenomics: application of genomics technologies in nutritional sciences and food technology. J. Food Sci. 66, 772–780 3 Smid, E.J. et al. (2005) Functional ingredient production: application of global metabolic models. Curr. Opin. Biotechnol. 16, 190–197 4 Raamsdonk, L.M. et al. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 5 Sumner, L.W. et al. (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–836 6 Vittal, R. et al. (2004) Gene expression changes induced by green tea polyphenol (K)-epigallocatechin-3-gallate in human bronchial epithelial 21BES cells analyzed by DNA microarray. Mol. Cancer Ther. 3, 1091–1099 www.sciencedirect.com
7 McLoughlin, P. et al. (2004) Transcriptional responses to epigallocatechin-3 gallate in HT 29 colon carcinoma spheroids. Genes Cells 9, 661–669 8 Fuchs, D. et al. (2005) Genistein blocks homocysteine-induced alterations in the proteome of human endothelial cells. Proteomics 5, 2808–2818 9 Fuchs, D. et al. (2005) Genistein reverses changes of the proteome induced by oxidized-LDL in EA.hy 926 human endothelial cells. J. Proteome Res. 4, 369–376 10 Herzog, A. et al. (2004) Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mRNA expression profiling. Proteomics 4, 2455–2464 11 Wenzel, U. et al. (2004) Protein expression profiling identifies molecular targets of quercetin as a major dietary flavonoid in human colon cancer cells. Proteomics 4, 2160–2174 12 Kitajka, K. et al. (2004) Effects of dietary omega-3 polyunsaturated fatty acids on brain gene expression. Proc. Natl. Acad. Sci. U. S. A. 101, 10931–10936 13 Lapillonne, A. et al. (2004) Polyunsaturated fatty acids and gene expression. Curr. Opin. Clin. Nutr. Metab. Care 7, 151–156 14 Kindermann, B. et al. (2005) Effects of increased cellular zinc levels on gene and protein expression in HT-29 cells. Biometals 18, 243–253 15 tom Dieck, H. et al. (2005) Transcriptome and proteome analysis identifies the pathways that increase hepatic lipid accumulation in zinc-deficient rats. J. Nutr. 135, 199–205 16 Johnson, A. and Manor, D. (2004) The transcriptional signature of vitamin E. Ann. N. Y. Acad. Sci. 1031, 337–338 17 Solanky, K.S. et al. (2005) Biofluid 1H NMR-based metabonomic techniques in nutrition research – metabolic effects of dietary isoflavones in humans. J. Nutr. Biochem. 16, 236–244 18 Wang, Y. et al. (2005) A metabonomic strategy for the detection of the metabolic effects of chamomile (Matricaria recutita L.) ingestion. J. Agric. Food Chem. 53, 191–196
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33 Lindon, J.C. et al. (2005) Summary recommendations for standardization and reporting of metabolic analyses. Nat. Biotechnol. 23, 833–838 34 Deutsch, E.W. et al. (2005) Human plasma PeptideAtlas. Proteomics 5, 3497–3500 35 Omenn, G.S. et al. (2005) Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–3245 36 Ping, P. et al. (2005) A functional annotation of subproteomes in human plasma. Proteomics 5, 3506–3519 37 Deshane, J. et al. (2004) Proteomics analysis of rat brain protein modulations by grape seed extract. J. Agric. Food Chem. 52, 7872–7883 38 German, J.B. et al. (2003) Personal metabolomics as a next generation nutritional assessment. J. Nutr. 133, 4260–4266 39 Lindon, J.C. et al. (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9, 1–31 40 van der Greef, J. et al. (2004) The role of analytical sciences in medical systems biology. Curr. Opin. Chem. Biol. 8, 559–565 41 Oresic, M. et al. (2004) Phenotype characterization using integrated gene transcript, protein and metabolite profiling. Appl. Bioinformatics 3, 205–217 42 Fairweather-Tait, S.J. (2003) Human nutrition and food research: opportunities and challenges in the post-genomic era. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358, 1709–1727 43 German, J.B. and Watkins, S.M. (2004) Metabolic assessment – a key to nutritional strategies for health. Trends Food Sci. Technol. 15, 541–549 44 Eisenbrand, G. (2004) Criteria for the evaluation of functional foods – opinion of the Senate Commission on Food Safety (SKLM) of the German Research Foundation (DFG) – (shortened version). Mol. Nutr. Food Res. 48, 541–544 45 Katan, M.B. and De Roos, N.M. (2004) Promises and problems of functional foods. Crit. Rev. Food Sci. Nutr. 44, 369–377 46 Katan, M.B. (2004) Health claims for functional foods. BMJ 328, 180–181 47 Bollard, M.E. et al. (2005) NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed. 18, 143–162
Articles of interest in other Trends and Current Opinion journals Nanotechnologies for biomolecular detection and medical diagnostics Mark Ming-Cheng Cheng et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2006.01.006 The amplified peptidome: the new treasure chest of candidate biomarkers David H. Geho et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2006.01.008 Genomics of microRNA V. Narry Kim and Jin-Wu Nam Trends in Genetics doi:10.1016/j.tig.2006.01.003 Chromatin regulation of virus infection Paul M. Lieberman Trends in Microbiology doi:10.1016/j.tim.2006.01.001 Recent advances of protein microarrays Claus Hultschig et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2005.12.011 www.sciencedirect.com
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Nutrition and food science go genomic Manuela J. Rist, Uwe Wenzel and Hannelore Daniel Molecular Nutrition Unit, Department Food and Nutrition, Technical University of Munich, Am Forum 5, D-85350 Freising-Weihenstephan, Germany
The wealth of genomic information and high-throughput profiling technologies are now being exploited by scientists in the disciplines of nutrition and food science. Diet and food components are prime environmental factors that affect the genome, transcriptome, proteome and metabolome, and this life-long interaction defines the health or disease state of an individual. For the first time the interaction of foods, and individual food constituents, with the biological systems can be defined on a molecular basis. Profiling technologies are used in basic-science applications for identifying the mode of action of foods or particular ingredients, and are similarly taken into the science-driven development of foods with a defined biofunctionality. Biomarker profiles and patterns derived from genomics applications in humans should guide nutrition and food science in developing evidence-based dietary recommendations and health-promoting foods.
Introduction In view of the emerging importance of nutrition in the development of chronic diseases, both academia and the food industry face a new challenge: the need to develop strategies and products that are not only safe but also contribute to health maintenance or can even prevent the development of specific diseases. Conceptually, this is achieved by identifying foods or food ingredients that affect the metabolic processes underlying disease initiation and/or progression and using this knowledge to develop products that functionally target metabolic perturbations. The wealth of genomic information, genomics-based technologies and model systems available provide a spectrum of new tools for use in the fields of human nutrition and food science. These new technologies are being used to study the molecular basis of the interaction of individual food constituents with both the genome and the metabolism of the human consumer. For historical reasons, nutrition and food science are not well prepared for exploiting genomics technologies, primarily because of the lack of appropriate teaching of human genetics, genomics and molecular biology in most university programs. However, these deficits have been recognized and, in response, numerous initiatives have been launched, recently, in Europe, Asia and the USA under the heading of ‘nutrigenomics’. Although, to some, nutrigenomics might represent just another ‘-omic’, it will change the face of the research in nutrition and food Corresponding author: Daniel, H. ([email protected]). Available online 20 February 2006
science by moving the genome into the centre of all the processes that essentially determine mammalian metabolism in health and disease [1,2]. Every nutritional process relies on the interplay of a large number of proteins, which are encoded by the respective mRNA molecules expressed in a certain cell, organ or organism. Alterations in mRNA levels and, in turn, of the corresponding protein levels (although these do not necessarily change in parallel) are crucial parameters in controlling the flux of a nutrient or metabolite through a biochemical pathway. Nutrients and non-nutrient components of foods, diets and lifestyle – including physical activity – can affect every step in the flow of genetic information from gene expression to protein synthesis and degradation and, thereby, alter metabolic functions in complex ways. There can be no doubt that the interplay of the somewhat static mammalian genome with its rapidly changing nutritional environment, represented by a huge spectrum of foods, is one of the most attractive and interesting areas in postgenomic research. We will use genomics as a collective term that covers the three sub-disciplines of transcriptomics, proteomics and metabolomics, and the term systems biology as the integrated approach for studying biological systems, at the level of cells, organs or organisms, by measuring and integrating genomic, proteomic and metabolic data. In this respect, it is the attempt to bring the existing knowledge about the various biological components onto a systemic level (with its unifying organizational principles) that determines the function of the organism. In this review, we will focus on the tools available to molecular food researchers and provide examples of what can be learned when nutrition ‘goes genomic’. The responses to food intake represent systems biology ‘par excellence’ It has been essential for the survival of mammals that they can adapt, quickly, to changes in their nutritional environment while retaining a sufficiently active metabolism to satisfy the requirement for a high rate of ATP production and to produce all the building blocks necessary for cell and tissue renewal and maintenance. Adaptation to food availability in terms of energy, in addition to individual nutrients, requires fast but also sustained responses that simultaneously change the numerous interconnected metabolic processes – cells need to regulate nutrient transport processes and storage capacity, tune the flux of intermediates through metabolic routes and branching points, and restructure the cellular transcriptome and proteome. The key question is whether
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Foods and diet Carbohydrates, Lipids, Proteins Fatty acids Retinoids Vitamin D3
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Figure 1. The complexity of the interplay of diet and foods with the genome that orchestrates human metabolism. The particular composition of a diet, in addition to individual foods or food constituents, can affect every step from epigenetic modifications of genes to changes in transcription, translation, protein degradation and the metabolome, by direct and indirect routes (e.g. diet-dependent hormone secretion, allosteric regulation). Profiling technologies, such as transcriptomics, proteomics and metabolomics, should enable the elucidation of the interactions of dietary constituents with the genome at every level of complexity and provide insights into the connectivity among the different layers of the biological processes.
the ‘omics’ technologies, combined with advanced data analysis and interpretation tools, enable understanding and reproduction of these physiological sensing and signal integration mechanisms and their multidimensional wiring. Assessing the metabolic responses to complex foods is, therefore, similar to looking at hundreds of test compounds, simultaneously, and observing diverse temporal and spatial responses. In this respect, nutrigenomics is clearly different from pharmacogenomic or toxicogenomic approaches where, in most cases, a single drug or xenobiotic is studied. The simplified scheme shown in Figure 1 represents the complexity of interactions among diet (or foods) and the mammalian genome, for example, epigenetic alterations of the DNA, effects on mRNA expression, control of the proteome, allosteric regulation and maintenance of metabolite pools – and their internal wiring – with feed-back and feed-forward loops. In the past, nutrition and food research focused almost exclusively on metabolites, which involved measuring the concentration of a few individual intermediates or markers, whereas the new molecular tools enable insight into virtually every step in the processing of the biological information: from DNA to mRNA to proteins and to metabolic function. However, there are important levels of biological regulation beyond gene and protein expression, for example, protein–protein interactions and alterations of protein activity by metabolic intermediates such as kinetic effects and allosteric modulation of function www.sciencedirect.com
(Figure 1). Consequently, whatever information is gathered at the mRNA and protein-expression levels will not enable sufficient predictions to be made on all metabolic consequences. The final stage along the line from gene to mRNA to protein to function is, therefore, the analysis of the pattern, and the concentrations, of the metabolites that flow between the proteins, organelles, cells and organs. That means that, ultimately, nutrition and food scientists will end up where they started – analyzing metabolites – but this time the analysis of the metabolome will comprise the sum of all detectable low- and intermediate-molecular-weight compounds rather than individual metabolites. The potential of comprehensive metabolic analysis, together with statistical methods of cluster analysis for discriminatory phenotype analysis, becomes obvious when reviewing the achievements in research into bacterial systems, yeast and plants – both wild-type and transgenic organisms [3–5]. The systems biology approach uses all genomic information, all expression information (at mRNA and protein level) and all metabolites to describe metabolism in the most comprehensive way, based on the networks of biological regulation.
Genomics applications in preclinical nutrition and food research The applications of transcriptomics, proteomics and metabolomics technologies in nutritional studies seem
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unlimited when it comes to basic and preclinical research in either cell cultures or animal models. The techniques have the potential to identify specific markers (biomarkers) that respond easily to a given nutrient, nonnutrient compound, treatment or diet, in a well-defined experimental setting. This might not mean that changes in mRNA or protein level can be taken as causal markers but rather as a pattern of expressed mRNAs, or a pattern of proteins, that changes in a characteristic but reproducible way. So far, biomarkers have been identified mainly by rational approaches based on the knowledge of metabolism; by contrast, the new screening processes are not knowledge guided, analyzing up to several thousand potentially affected indicators of the metabolic status of an individual, simultaneously. There are a growing number of examples of DNA microarray and proteome profiling technologies being used to identify the cellular responses to dietary constituents and for identifying their molecular targets. For example, studies on flavonoids, such as the green tea catechins [6,7] or soy isoflavones and flavones [8–11], try to link their bioactivity to the findings of epidemiological studies that suggest that secondary plant metabolites possess a variety of healthpromoting activities. Selected nutrients, including polyunsaturated fatty acids [12,13] or micronutrients such as zinc [14,15] and vitamin E [16], have also been studied in cell culture systems or laboratory animals – by transcriptome, proteome and metabolite profiling – to define the molecular mechanisms by which they affect metabolism, and for deriving signatures that describe a particular metabolic state. There can be no doubt that, in the near future, we will be overwhelmed with similar studies that investigate the biology of individual food constituents. Bioinformatics and chemometrics are essential to cope with the huge amounts of data already being generated, and more powerful tools need to be developed to facilitate analysis and interpretation of the results of comprehensive ‘omics’ studies. Current limitations of the ‘omics’ technologies when applied to studies in humans Although the applications of the different ‘omics’ technologies appear unlimited when using either cells in culture or model organisms, clearly there are restrictions on their use for studies in humans. Expression profiling at the level of mRNA or protein is limited by the availability of vital cells or tissues for analysis. Although certain tissues and cells, such as hair-follicle cells, skin cells or even exfoliated intestinal cells, can be obtained in sufficient quantities by non-invasive techniques, proteome or metabolome analysis cannot be carried out on the singlecell level, whereas expression analysis using quantitative PCR or array techniques can be performed on single cells. Different types of blood cells are, therefore, an interesting source of biological material and can be used as reporter cells in human studies. These cells respond to dietary fluctuations and, more interestingly, have different lifetimes, different gene expression profiles and control systems, and can reach and occupy different body compartments. In particular, human peripheral blood mononuclear cells are a source for the identification of www.sciencedirect.com
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mRNA or protein biomarkers, which potentially change in response to environmental factors, by using DNA-arrays or proteome analysis. The biological samples that can be obtained most easily in the required amount are urine or saliva (by non-invasive sampling) and serum or plasma (by minimal invasive approaches). Given that these biological fluids are essentially devoid of cells (and mRNA and DNA), only proteins and metabolites can be analyzed with the required robustness and sensitivity. Profiling technologies of body fluids for use in human studies Plasma and serum represent the fluid compartment that carries metabolites and signaling molecules to and from cells in the various organs and, therefore, serve as a distribution system in interorgan metabolism. By contrast, urine excretion serves to eliminate metabolic endproducts of no further use and contributes to electrolyte and water homeostasis. Nutrients and metabolic intermediates are found in urine only in trace quantities because of the efficient reabsorption mechanisms in the tubular cells along the nephrons in the kidney; however, their appearance in higher concentrations might reflect a disturbed homeostasis or impaired renal function. Although well established in bacterial, yeast and plant systems, metabolomics studies for assessing the effects of diet or food constituents in humans are sparse and are currently based mainly on NMR spectroscopy applications [17,18]. Metabolite profiling The most important techniques for metabolomic applications in human biofluids are gas chromatography (GC) or liquid chromatography (LC), coupled to mass spectrometry (MS) or NMR spectroscopy. Each of these platforms has advantages and limitations, which have been reviewed in detail elsewhere [19–21]. To obtain the most valuable information from the metabolome, a combination of different techniques appears necessary because each technique covers different mass and concentration ranges; for example, GC–MS is mainly used to identify and quantify low-molecular-weight metabolites with masses of up to w1000 Da. When applied to human biofluids, GC–MS has been used in toxicology, pharmacology and neonatal screening, and only a few selected metabolites are usually analyzed [22–24]. In nutrition studies, GC–MS has been used to determine the fate of specific food compounds or metabolites [25,26]. LC–MS seems better suited for the analysis of labile and low- to intermediate-molecular-weight compounds. It is the most global of the techniques described here and can be used to determine a raw metabolite profile, in addition to identifying and quantifying specific compounds. Absolute quantification, however, requires reliable internal standards and better results for identification can be obtained using tandem MS (LC–MS/MS). Several studies have been published using LC–MS for the analysis of selected compounds [26,27] or metabolite profiles [28]. High-resolution 1H-NMR spectroscopy can, in principle, detect all proton-containing metabolites present in a sample above a certain threshold concentration. Thus,
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one-dimensional 1H-NMR techniques are mainly used for metabolic fingerprinting of raw samples without identifying the individual metabolites. After chromatographic separation, or using two-dimensional techniques, NMR can also be used to characterize specific compounds or to obtain valuable structural information. NMR techniques have been used, successfully, to analyze metabolite changes in human and animal body fluids and tissue extracts under various conditions [29–32]. The huge amount of data generated from the described metabolomics technologies requires processing and analysis using appropriate chemometric methods. Following the necessary pre-processing of the spectra, relevant information is usually extracted and visualized using multivariate analysis methods such as hierarchical cluster analysis, principal components analysis or discriminate analysis [20,28]. Various efforts are being undertaken to standardize metabolomics experiments and build databases that will facilitate the identification of metabolites [33] (for the different initiatives see http:// www.niddk.nih.gov/fund/other/metabolomics2005/local. htm and http://www.nugo.org/metabolomics/13187). Despite great progress in the field of metabolomics, profiling techniques applied to human body fluids in nutrition and food research face some unique challenges (Box 1), which also require a special collaborative effort to advance the science.
Proteome profiling Classical two-dimensional gel-electrophoresis separations for the plasma proteome, combined with peptide-mass fingerprinting using matrix-assisted laser desorption/ ionization–time-of-flight–MS (MALDI–TOF–MS) for protein identification, in addition to shotgun approaches with protease digestion followed by LC-based MS technologies for peptide mapping, are all applied to define the plasma proteome [34–36]. In a huge consortium, under the umbrella of the Human Proteome Project (http://www. hupo.org), different proteome analysis techniques and methods for standardization and sample processing are being developed to define the human plasma proteome. The first human plasma proteome database (http://www. plasmaproteomedatabase.org) has been launched recently. This can serve as a reference for profiling and protein identification when plasma proteomics studies are carried out in nutrition and food research, although, except for a few examples using cell culture models or laboratory animals [8,37], such studies are currently lacking. However, in view of the numerous efforts to define the plasma proteome for a variety of clinical applications, the wealth of information gathered here deserves its use in nutrition and food research, particularly because a variety of plasma proteins can be used to describe the nutritional status of an individual. Moreover, the plasma sub-proteomes, formed by proteins and peptides released by individual organs and cell populations, might be useful to define the physiological state of these organs and/or cells and are, therefore, of particular relevance for nutrition and food research as diseaserelated entities [36]. www.sciencedirect.com
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Box 1. The subtleties of metabolomics approaches in human studies Human metabolism is essentially the expression of a transient steady state in the dynamics of the biosynthesis and degradation of proteins (turn-over) that function either as enzymes, receptors, transporters, channels, hormones and other signaling molecules or that provide structural elements for cells, organs or the skeleton. Between the proteins, there is a variable flow of metabolic intermediates, which serve as building blocks or provide the fuel for ATP synthesis, and these can be determined by metabolomics approaches. However, the metabolome is variable in space and time. Every organ, different cells within an organ and different intracellular compartments display different metabolite compositions, which, again, are different from the metabolite profiles in blood or other body fluids: every sample taken represents just a snapshot of an ever-changing metabolite profile. The variability of metabolite profiles appears much greater in humans than in model systems or laboratory animals [28,30,47]. There are many intrinsic and extrinsic factors affecting the metabolome, such as genotype, gender, hormonal status and age, in addition to dietary variation, physical activity, socio-economic status, cultural influences, medication, smoking, stress, pathologies and, importantly, the gut microflora with its enormous metabolic capacity. When metabolite profiling is applied to human body fluids, it has to be taken into account that plasma and urine are just flow-through compartments in which thousands of nutrients and metabolites provided by the diet after digestion, absorption and clearance by the liver are mixed with those released by the various organs and cell types, including those derived from microbial metabolism. Metabolomics, as applied to human studies, therefore faces the problem to sort out immediate diet effects and the effects of the microbial flora on the metabolite profiles, in whatever sample, to resolve the ‘endogenous fingerprint’. It, therefore, requires particularly well-defined experimental studies in volunteers kept under controlled feeding and/or starvation regimens in a standardized setting – a concept unique to this application of metabolomics. Moreover, there is a necessity to standardize the application of metabolomics to nutrition studies in terms of sample collection, preparation of fluids, times, volumes and processing aids. For all of this to happen, an international collaborative project needs to be launched.
The concept of metabolic-marker profiles in nutrition and food research Gomeostatic mechanisms in biological systems are characterized by hierarchical orders and multiple redundancies to maintain a given steady state for as long as possible. Any disturbance in the system is compensated for in space and time, and even the malfunction of a gene, a protein or even a whole pathway might be overcome without evident phenotypic alterations. Therefore, it is obvious that a single marker – whether a transcript, protein or metabolite – is often unable to provide sufficient information on the metabolic state. However, subtle changes, which reflect the biological redundancy in a large spectrum of mRNA species, protein entities or metabolites, can be observed and might, conceptually, as metabolic-marker profiles, describe the metabolic status representative of a given state of health or disease stage. It is becoming increasingly apparent that such early markers for disease initiation or progression can be identified by biomarker profiling and using different profiling technologies [38– 40]. Figure 2 depicts the concept of metabolic-marker profiles to describe the metabolic status of an individual in the course of disease development. Although only a few scientific publications have taken this concept to proof [29,41], there are numerous studies currently exploring
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Figure 2. Time-dependent changes of metabolic-marker profiles during progression from a healthy to a diseased state. Markers can be derived from transcriptome, proteome or metabolite profiling, or combinations thereof, and can improve diagnosis. Whenever the markers leave the range of natural variation, they are predicted to indicate early preclinical alterations, which might not be detected otherwise. At this stage, early interventions, such as a change in diet and physical activity or the application of functional foods, might possibly prevent the onset of disease, whereas in the pathological state, usually a pharmacological therapy is required.
metabolic-marker profiles in research projects linked to human nutrition and food effects on metabolism. For example, the metabolic effects of dietary isoflavones or chamomile ingestion have been studied using the NMR analysis of human biofluids [17,18]. Taking genomic technologies into product development There can be no doubt that genomic technologies will change the face of food research [42,43]. Although already in use in all areas of food biotechnology and microbiology, these profiling tools are only used, currently, in cell culture and laboratory animal studies in the process of developing supplements or food products that perform specific functions (functional food), including new transgenic plant varieties [7,10,12]. However, in view of an emerging demand for evidence-based health claims related to functional food [44,45], genomics-based platforms and biomarker-profiling techniques also need to be applied to human studies. Product development should be guided by scientific data and sound biological evidence, which might be derived from models of increasing biological complexity until finally including human intervention studies (Figure 3). It is agreed by expert panels and food authorities in a variety of countries [46] that the development of functional foods, and their safety assessments, should be conducted on a case-by-case basis using studies with good experimental design. The totality of scientific evidence should eventually enable agreement within the scientific community that an association between a food or a food component and a proposed health benefit is valid [45]. From this perspective, product development in the functional food era needs to apply www.sciencedirect.com
genomics technologies because they provide a comprehensive analysis of the biological functionality of a given ingredient, a mixture or a complex food, not only with respect to the targeted biochemical processes but also with respect to the identification of unwanted side effects in the overall safety assessment. Where do we stand, and where do we go from here? Genome–food interactions are the paradigm for the interplay between the human genome and its environment. Nutrition and food science are stepping into the genomics era, and it is becoming evident that nutrients and other food components are key factors in altering gene transcription, protein levels and functions, and the metabolome, which eventually translates into a health or disease state on the basis of a given genome. A wealth of genetic information, and novel techniques with highthroughput capabilities, will provide new tools for nutrition and food research. Knowledge regarding the response of mammalian organisms to changes in diet or in response to individual nutrients and non-nutrient components of foods can be gathered by expression arrays, proteome analysis and metabolite profiling technologies. Emerging technologies, such as two-dimensional magnetic resonance imaging, currently under development might also be applied. All of these techniques should enable the determination of metabolic markers that can guide the assessment of the health status of humans and provide measures for food-derived effects on human metabolism. In terms of the development of healthpromoting and functional foods that specifically target a metabolic process to slow disease progression and reduce
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Figure 3. Applications of genomics technologies when applied to the development of functional foods that target specific body functions through either individual ingredients or mixtures. Conceptually, a discovery process that derives molecular markers for the bioactivity of defined foods or food ingredients, and identifies their putative mode of action using appropriate cell models, should be followed by an intervention study in an animal model to verify and/or define the markers in vivo. A human intervention trial with a complex food that employs the biomarker profiling concept should follow for proof-of-concept and to substantiate health claims on a scientific basis. This process might well be iterative and it might be necessary to return to cell culture or animal models to find or validate relevant markers and to be able to substantiate the health claims arising from human intervention trials.
risk, metabolic-marker profiles need to guide product development in an evidence-based manner. The time has now arrived to take the new technologies into well-defined human intervention trials and large-scale cohort studies, with groups of defined genotypes, for proof-of-concept. Although these studies will be tedious and costly, in view of the rapidly evolving health problems in all developed countries, such as obesity, diabetes type II, cardiovascular diseases and cancers, with well-defined causes – an unhealthy lifestyle, overnutrition and lack of exercise –these studies seem to warrant concertive and collaborative action by academia and industry. References 1 Muller, M. and Kersten, S. (2003) Nutrigenomics: goals and strategies. Nat. Rev. Genet. 4, 315–322 2 van der Werf, M.J. et al. (2001) Nutrigenomics: application of genomics technologies in nutritional sciences and food technology. J. Food Sci. 66, 772–780 3 Smid, E.J. et al. (2005) Functional ingredient production: application of global metabolic models. Curr. Opin. Biotechnol. 16, 190–197 4 Raamsdonk, L.M. et al. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 5 Sumner, L.W. et al. (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–836 6 Vittal, R. et al. (2004) Gene expression changes induced by green tea polyphenol (K)-epigallocatechin-3-gallate in human bronchial epithelial 21BES cells analyzed by DNA microarray. Mol. Cancer Ther. 3, 1091–1099 www.sciencedirect.com
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Articles of interest in other Trends and Current Opinion journals Nanotechnologies for biomolecular detection and medical diagnostics Mark Ming-Cheng Cheng et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2006.01.006 The amplified peptidome: the new treasure chest of candidate biomarkers David H. Geho et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2006.01.008 Genomics of microRNA V. Narry Kim and Jin-Wu Nam Trends in Genetics doi:10.1016/j.tig.2006.01.003 Chromatin regulation of virus infection Paul M. Lieberman Trends in Microbiology doi:10.1016/j.tim.2006.01.001 Recent advances of protein microarrays Claus Hultschig et al. Current Opinion in Chemical Biology doi:10.1016/j.cbpa.2005.12.011 www.sciencedirect.com